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Paper Citation Record · LEDGER

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN

As of 22 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2606.06261.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.06261 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T23:14:45.350459Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b15ded12-e5e7-4657-a7bf-de8e7d8699a0 · outbound

This paper cites O-RAN Security Threat Modeling and Remediation Analysis 4.0,.

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN O-RAN Security Threat Modeling and Remediation Analysis 4.0,

Reference 1

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unresolved
no resolver link, observed 2026-06-27T23:14:45.350459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T23:14:45.350459Z digest=sha256:ed30359125ac284ebcb2f9eb433927cb5b6e72e5e05a7f26caf5a57be3cc9dab

Observation 93d2fa8e-a492-4d50-9b62-c3effb59c7b7 · outbound

This paper cites A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data,.

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data,

Reference 2

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unresolved
no resolver link, observed 2026-06-27T23:14:45.350459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T23:14:45.350459Z digest=sha256:aa50f37a81230ec1545111b5cdb7ed8dcb26f13fd99f5a9d6535c27ba6d0bf0a

Observation ff2e3893-25a0-43d3-8226-8c39b5d3d587 · outbound

This paper cites Marina, and Bozidar Radunovic.

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN Marina, and Bozidar Radunovic

Reference 3

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metadata mismatch
arxiv_id, observed 2026-06-27T23:21:23.502009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-27T23:14:45.350459Z digest=sha256:8f6d4b9f403de67b87b574ae97618eca129d851c0acab45b674938d3b1db307a

Observation 57c13196-843f-4d7c-966c-ccaa6081d955 · outbound

This paper cites Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection,.

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection,

Reference 4

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metadata mismatch
arxiv_id, observed 2026-06-27T23:21:23.504494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-27T23:14:45.350459Z digest=sha256:6d15882de635433c99f6a5348f186da5a3387906525ae0730f160fb52210fc03

Observation 49593be0-64e0-4dbb-b2b4-1d2ed2d523d0 · outbound

This paper cites Harnessing Vision- Language Models for Time Series Anomaly Detection,.

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN Harnessing Vision- Language Models for Time Series Anomaly Detection,

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T23:14:45.350459Z digest=sha256:90d700e915f74e4d07ecdc855cc8c7a6bb660d10a2b6c02ce146b9941e6ffc61

Observation 04fbc205-c9d3-4d31-8f0f-089586f8d9f5 · outbound

This paper cites Can Multi- modal LLMs Perform Time Series Anomaly Detection?.

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN Can Multi- modal LLMs Perform Time Series Anomaly Detection?

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T23:14:45.350459Z digest=sha256:96fe4a6a56d57d2682063329895052cbaaa4a5bbdc5fa4e1ae9579b3cdfb79f8

Observation 81bb976f-43d6-49d1-88af-45f0a5c9c46e · outbound

This paper cites Effective troubleshooting,.

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN Effective troubleshooting,

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T23:14:45.350459Z digest=sha256:4a94be74479efc79ef8fd0fc240095c80a05d6a225d1051b670d47c503258ba5

Observation 3461862e-69e0-48bc-abde-96ff4beeb9db · outbound

This paper cites Towards 6G: Architectural Innovations and Challenges in the ORIGAMI Framework,.

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN Towards 6G: Architectural Innovations and Challenges in the ORIGAMI Framework,

Reference 8

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no resolver link, observed 2026-06-27T23:14:45.350459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T23:14:45.350459Z digest=sha256:be5fe38bd6e1c44c492b7551b81f03d01b064744668c0b480053fe38ec899583

Observation 9fa5e14b-a2ae-451b-9bfd-a5288e2fed5e · outbound

This paper cites Attacking O-RAN Inter- faces: Threat Modeling, Analysis and Practical Experimentation,.

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN Attacking O-RAN Inter- faces: Threat Modeling, Analysis and Practical Experimentation,

Reference 9

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unresolved
no resolver link, observed 2026-06-27T23:14:45.350459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T23:14:45.350459Z digest=sha256:8e2588eb9806a18aae470f8531414a903670d68c5d36139d97747bd62b65da12

Observation 136b0e76-57ef-4b23-94ff-d9318fbeecc4 · outbound

This paper cites TESSERACT: Eliminating Experimental Bias in Malware Classifica- tion across Space and Time,.

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN TESSERACT: Eliminating Experimental Bias in Malware Classifica- tion across Space and Time,

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T23:14:45.350459Z digest=sha256:616289c04df786e89dbf95c515394384aa0ac1067f886170190932cfd2f67c9d

Observation 1a5ca96b-60b3-4277-8210-08f4e715b61b · outbound

This paper cites See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers.

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers

Reference 11

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T15:57:06.938409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-27T23:14:45.350459Z digest=sha256:b4486ebf02bb46feed942e7c8badf1aeb17d52fbc56792c7c8b07e80b19678e3

Observation 88d3a8ac-2feb-4d33-8bcb-b48cce56ec10 · outbound

This paper cites AI- on-RAN for cyber defense: An XAI-LLM framework for interpretable anomaly detection,.

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN AI- on-RAN for cyber defense: An XAI-LLM framework for interpretable anomaly detection,

Reference 12

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no resolver link, observed 2026-06-27T23:14:45.350459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T23:14:45.350459Z digest=sha256:280685cccee2247dfdb0b909f32e35b89c1b60cdfad9331c10d7b480b171f82e

Observation 1980e65d-41af-400b-889e-4eb3808cba15 · outbound

This paper cites FALCON: An Accurate Real-Time Monitor for Client-based Mobile Network Data Analytics,.

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN FALCON: An Accurate Real-Time Monitor for Client-based Mobile Network Data Analytics,

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T23:14:45.350459Z digest=sha256:532df66b49168c155c04858d9022d71b43ae122d3dab1bed3e321c39cfa6e943

Observation b3796e8a-e954-4bc3-b88b-15b500e11f0b · outbound

This paper cites Po- sition: Quo Vadis, Unsupervised Time Series Anomaly Detection?.

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN Po- sition: Quo Vadis, Unsupervised Time Series Anomaly Detection?

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T23:14:45.350459Z digest=sha256:76e6b135644ce4020811883728d73b32f8529deda51e505b3563559f0ef947ec

Observation a8c0e040-ef80-4683-a004-3d10bdcf58a6 · outbound

This paper cites Precision and recall for time series,.

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN Precision and recall for time series,

Reference 15

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unresolved
no resolver link, observed 2026-06-27T23:14:45.350459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T23:14:45.350459Z digest=sha256:67f8b4dbe4829a2f20df41389467534f8c5239d6d521aa9644ed192b4ab3ab9e

Pith citing papers

No inbound Pith citation observations are available.